Checkout lane alert system and method

ABSTRACT

A checkout lane alert system and method is a live, computer base, in-store system that integrates real time shopper traffic data with computerized statistical analysis to generate accurate short term forecasts of shopper traffic at the checkout lanes of the store. Shopper entry time data from a recognition system is used in the analysis. The system combines real time data with pre-gathered statistical data about the population that shops in a particular type of store and data which characterizes the checkout lane throughput capability for the store to predict checkout lane traffic and staffing requirements.

BACKGROUND AND SUMMARY OF INVENTION

This invention relates to a computer system and method for forecastingthe traffic at checkout lanes in mass merchandising stores, departmentstores, grocery stores, and other applications to give managementsufficient time to properly staff the checkout lanes to maximize laborefficiency and customer satisfaction.

Traditionally, the systems used by store management to staff checkoutlanes have been "reactive". By visually observing the length of thecheckout lines management would adjust the number the checkout personnelas needed. There are a number of problems associated with this approach.The store manager does not always notice an increase in lines untilafter customers have experienced excessive waiting in the lines. Theresult is customer dissatisfaction with the store and a substantiallikelihood of lost sales. There appears to be a direct relationshipbetween the number of purchases a customer will make and the length ofthe checkout lines. There also appears to be a relationship betweenwhether a customer will shop in a particular store and the length of thestore's checkout lines. Of course, the problem can be alleviated by overstaffing the checkout lanes, but this results in a waste of storepersonnel and increased overhead that reduces profits. Moreover, in manystores checkout personnel have other duties when they are not working atthe registers, so even when the manager observes excessive checkoutlines, it takes some period of time to bring additional employees on tothe registers from their present duties. In the meantime, customersbecome irritated and dissatisfied with a potential loss of business.

Another problem experienced with the reactive approach is that from acustomer relations point-of-view it is easier for the store managementto open checkout lanes than to then close them. Once a lane is open,management tends to keep the lane open for some period of time even iflane traffic diminishes resulting in an inefficient use of storepersonnel.

The checkout lane alert system of the present invention overcomes theseproblems by providing a "proactive" computer system and method thatpredicts lane traffic in the store and gives the store manager advancenotice so that adjustments in checkout lane staffing can be made thatwill prevent excessive checkout lines and excessive checkout lanes. Sorather than wait for the lines to begin building, and then reacting tothe build up, the system and method of this invention forecasts thestaffing requirement and allows proper staff deployment before a buildup begins. While traditional methods are mostly static in that stafflevels are predetermined, the system of this invention allows fordynamic staffing of the checkout lanes for more efficient allocation oflabor.

The checkout lane alert system of the present invention is a live,computer-based, in-store system that integrates real-time shoppertraffic data with computerized statistical analysis in order to generateaccurate short term forecasts of shopper traffic at the checkout lanesof the store. The system uses up to the minute traffic data to createits forecast and allows a retailer to track the momentary surges in lanetraffic and meet these with the proper staffing.

Generally, the system of the present invention comprises a person andobject recognition system component and a computer system runningappropriate software. The recognition system "recognizes" the potentialshoppers as they enter and leave the store by recognizing persons orobjects as they move past a selected location in the store andclassifying the persons or objects in accordance with selected criteria.Such a recognition system is disclosed in Frey, U.S. Pat. No. 5,138,638,the entirety of which is incorporated herein by reference, and isfurther disclosed in U.S. Pat. application Ser. No. 07/855,503, filedMar. 20, 1992, entitled "Person and Object Recognition System", (theentirety of which is incorporated herein by reference) which is acontinuation-in-part of the application which issued into U.S. Pat. No.5,138,638. The two components are connected by a cable that allowscommunication of the shopper traffic data from the recognition system tothe computer in realtime.

The computer that is connected with the recognition system runs thesoftware for the present invention. This software allows the computer toretrieve the shopper entry and exit time data from the recognitionsystem for use in its statistical analysis. The software combines thisreal-time data with pre-gathered statistical data about the populationthat shops in a particular type of store, and data which characterizesthe checkout lane throughput capability for a store. Each minute thesoftware performs many simulations which combine these factors indifferent ways in order to forecast the shopper traffic at the checkoutlanes for that minute and minutes that follow. The computer screengraphically displays the forecast, and the system updates the screendisplay with results of the simulations and alerts store personnel whenlane traffic will increase or decrease to a point where a new checkoutlane staffing level is needed.

The system of the present invention uses two pieces of information aboutthe shopping population in the store. The first is called the"conversion rate" which is the ratio of shoppers who actually buy anitem or items to the total shoppers in the store. This information isused to estimate the percentage of the total entering shoppers that willactually visit the checkout lanes. The second piece of informationconcerns the amount of time the shopper spends in the store. Thisinformation allows the system to forecast the arrival time of eachshopper at the checkout lane. The system uses a frequency distributionof the shopping times of shoppers in the store. During each simulationperformed by the system, shoppers are assigned pseudo-random shoppingtimes based on the frequency distribution. During the many repetitionsof the simulation, shoppers will be assigned different randomcombinations of shopping times and the final forecast is the average ofthe simulations.

The system also uses "optimal" service criteria for a particular storeand a frequency distribution of shopper checkout times (how long ittakes for a shopper to check out once the shopper reaches the register)to determine the number of checkout lanes required once the lane trafficis forecast. The frequency distribution of checkout times ispre-gathered automatically from the store point-of-sale (POS) checkoutequipment.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustrating the system of the present invention.

FIGS. 2,3A-3C, 5A-5B, are flow diagrams of the system of the presentinvention.

FIG. 6 is a typical frequency distribution of shopping times used in thesystem of the present invention.

FIG. 7 is a typical frequency distribution of shopper checkout timesused with the system of the present invention.

FIG. 8 is a typical screen display for the system of the presentinvention.

FIGS. 9A-9C are a flow diagrams for use with the system of the presentinvention for establishing a lookup table of checkout lane staffingrequirements versus checkout lane staffing forecasts.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

With reference to FIG. 1, there is shown a checkout lane alert system 10of the present invention comprising a shopper traffic recognition system12, and a computer 14. The recognition system 12 preferably is of thetype disclosed in U.S. Pat. No. 5,138,638 and U.S. No. 5,305,290,pending patent application Ser. No. 07/855,503, previously referred to.The recognition system determines the number of shopper units that enterthe store, a "shopper unit" being defined as a person expected to makepurchases in the store. The recognition system comprises one or moresensors that may be mounted above the store entry and exit doorways.When a person or object passes beneath these sensors, the systemmeasures the height, speed, and length of the person or object andgenerates signals representing a time-dependent height profile of theperson or object as the person or object moves past the sensor. Thesesignals are transmitted to a signal processor, and from there to acentral processing unit which processes the signals and generates datarepresenting the number of persons or objects in pre-selectedcategories. The system also determines the direction of movement of theperson Or object (in or out of the store). From a comparison withselected criteria, the system determines whether a particular personentering the store is a potential buyer as opposed, for example, to asmall child who is not likely to buy or an object such as a shoppingcart.

When a shopper who is a potential buyer enters or exits through adoorway monitored by the recognition system 12, the recognition systemrecords the time of the event and stores that information in an internalmemory buffer so that the information is accessible to the computer 14.

The computer 14 runs the software for the checkout lane alert system andallows the computer to retrieve the shopper entry time data from therecognition system 12 for use in its statistical analysis. As will befurther explained, the software combines the real-time data withpre-gathered statistical data about the population that shops in aparticular type of store and data which characterizes the checkout lanethroughput capability for the store to predict checkout lane staffingrequirements.

The system further comprises an optional communications link to a storepaging system or other alert mechanism to alert store management of theneed to change lane staffing, and an optional communications link to thestore point-of-sale (POS) equipment for a real-time update of storeparameters which may be used by the checkout lane alert software. Theseparameters may include shopping time, checkout processing time, andconversion rate, which are stored in a computer file 20 resident in thecomputer.

FIG. 2 shows the general flow diagram for the checkout lane alert systemof the present invention. It is a real-time processing loop which iscalled one time every minute that the program operates. In thispreferred embodiment of the invention, the module creates a forecast ofcheckout lane arrivals for the current minute through the next 20minutes. A shorter or longer forecast time may be used, but the accuracyof the forecast diminishes the greater the forecast time. A forecastperiod of 20 minutes has been found to provide acceptable accuracy withsufficient length to satisfy store conditions. In accordance with theprogram, the shopper traffic data from the recognition system 12 for aperiod of 30 seconds is retrieved through the communication link. Thelane alert system 10 then creates and updates a checkout lane trafficforecast for the current minute through the next 20 minutes. This willbe described in more detail with reference to the flow diagrams of FIGS.3-5. From data representing the updated forecast, an updated graphicdisplay of the traffic forecast is generated along with alert messagesfor lane staffing requirements. This processing is repeated during eachminute in real-time.

The portion of the diagram of FIG. 2 for creating and updating thecheckout lane traffic forecast for each minute from the current minutethrough the next 20 minutes is shown in more detail by the flow chartsof FIGS. 3-5. Referring to FIG. 3A, the system computes a large numberof simulations during each minute for purposes of updating the display.In this preferred embodiment the number of simulations is 200, althougha fewer or greater number may be used. It has been found that under mostconditions 200 simulations is sufficient to provide reliable data forupdating the display. So during each current minute the lane alertsystem operates in real-time to update shopper entry information fromthe recognition system 12 and compute 200 simulations for updating thedisplay. The process for performing the simulations will now bedescribed.

During each update the system goes back 60 minutes, and for each minuteof the past 60 minutes the recognition system has stored information onthe shoppers who entered the store during that minute. For each suchshopper it determines whether the shopper is a "buyer" or a "no-buyer"based on a "conversion rate". The conversion rate is the rate at whichshoppers turn out to be buyers. In other words, it is the percentage ofshoppers that are buyers. Obviously, this percentage changes over timeand from store to store, and in some cases from shift to shift. Theconversion rate is computed from data generated by the recognitionsystem 12 representing the time and event of each shopper leaving thestore during a given time interval, and data from the store's POS systemrepresenting the time and event of each buyer that checks out through acheckout lane during that same time interval. The POS system registerseach transaction at the checkout and records the time of thetransaction. A "transaction" is represented by a single buyer checkingout through a checkout lane, and not by the number of purchases. So withthe data from the POS system on the number of transactions over aparticular time period, and data from the recognition system on thenumber of shoppers exiting the store over the same time period, theconversion rate is computed as the number of transactions divided by thenumber of shoppers exiting the store. For example, if 100 people leavethe store during that period, and there are 36 transactions, theconversion rate is 36% meaning that 36% of the shoppers in the storewere buyers. Hence, the conversion rate is really a load factor as faras checkout staffing requirements are concerned. The checkout personneldo not have to serve every shopper, but rather only those shoppers thatbuy.

It has been found that the conversion rates are relatively constant fora given day and shift so it is possible to average those conversionrates and use the average rate for a given day and shift for purposes ofthe forecast. The conversion rate can be computed with greatergranularity if desired, such as for each hour of each day, but thatdegree of granularity may not be necessary. As another alternative, thesystem may compute the conversion rate from a frequency distributionbell curve of conversion rates using data from the recognition system 12and the store POS system with the lane alert system running a number ofsimulations by randomly selecting conversion rates from the bell curveand then averaging those results to give final predictions. However, ithas been found that in most cases the first method described above,using a single average of conversion rates, will suffice.

For each shopper entering the store during each minute of the past 60minutes as recognized by the recognition system 12, the system 10designates the shopper as either a "buyer" or a "no-buyer" based on thedetermined conversion rate. For example, if the conversion rate is 36%,there is a 36% chance that the shopper will be designated as a buyer,and a 64% chance that the shopper will be designated as a no-buyer (FIG.3B).

As shown in FIG. 3C, if the shopper is designated as a buyer, theshopper is assigned an arbitrary value of shopping time from a bellcurve, such as that of FIG. 6, from which the shopper's minute arrivaltime at the checkout lane is calculated. The minute arrival time is theminute index (the time the shopper entered the store as detected by therecognition system) plus the shopping time randomly assigned from thebell curve. This process is repeated for each successive shopper thatentered the store during the first minute of the past 60 minutes, andthen is further repeated for each successive shopper that entered thestore during each successive minute during the past 60 minutes. Finally,the process is then repeated 200 more times, each time beginning withthe first shopper entry of the first minute and ending with the lastshopper entry of the last minute until all 200 additional cycles arecompleted. With each cycle each shopper entry is designated as a "buyer"or "no-buyer" based on the conversion rate, and each is randomlyassigned a shopping time from the bell curve. As the simulations arecomputed, the number of buyers that arrive at the checkout lanes duringeach minute from minus 20 minutes to the present minute and from thepresent minute to plus 20 minutes is incremented or totaled, and thetotals for each minute divided by the maximum simulations (in thisembodiment 200 simulations), to arrive at the total number of buyersarriving at the checkout lanes for each minute from minus 20 to plus 20minutes (FIG. 4). After it has completed all simulations, the systemclears the Graphic display and sets up a new display which graphs theresults of the simulations from minus 20 to plus 20 minutes. A typicalsuch graph is shown in FIG. 8. So, for each of the 40 minutes the systemcomputes based on the simulations the number of buyers to arrive duringeach minute interval and plots the information on the graphic display.

One way to derive the shopping time bell curve is to use visualobservation and to time shoppers in a variety of stores and under avariety of shopping conditions. Data representing the bell curve are inthe computer file 20 and the computer randomly selects shopping timesfrom the curve to assign to buyers entering the store. Other methodsalso may be used in creating a shopping time bell curve such as bycomparing shopper traffic peaks entering the store with shopper trafficpeaks exiting the store over a period of time using the recognitionsystem 12.

In addition to producing a graphic display of predicted lane arrivalsover a selected period, the system also computes the need for staffingchanges at the checkout lanes in response to the predicted checkout lanetraffic and other criteria. The display screen displays alert messagestelling the operator whether staffing changes are needed. In predictingstaffing changes several factors come into play. One such factor relatesto the weight to be given the lane traffic data for each period of timefor the time interval over which the lane traffic is displayed. It hasbeen found that greater weight should be given to the lane trafficcloser to the present time than to the lane traffic expected at timesfurther in the future. Therefore, for purposes of determining whether toopen or close checkout lanes, it has been found desirable to weight theaverages in favor of those near the current time. In accordance withthis embodiment of the invention, the average lane traffic T1' for minus5 to plus 4 minutes is weighted at 55%, the average traffic T2' for plus5 to plus 9 minutes is weighted 30%, and the average traffic T3' forplus 10 to plus 14 minutes is weighted 15%. The sum of these produces aweighted average of lane traffic from minus 5 to plus 14 minutes. Thesystem then predicts staffing requirements from a look-up table ofstaffing required for this weighted average traffic.

Although the comparison with the look-up table indicates a staffingchange, it has been found desirable to make such a change only if thecondition dictating the change has existed for some period of time. Thisprevents excessive staffing changes resulting from overreaction totemporary lane traffic of short duration. Thus, in accordance with thisembodiment of the invention, the staff requirement for the presentminute is compared with the staff requirement for the prior two minutes.The system generates an alert message on the screen telling the operatorto increase lane staffing as indicated by the present minute only ifthat staffing is greater than that of the prior two minutes. The systemdisplays a message on the screen telling the operator to decrease thelane staffing only if the system indicates a staff level for this minuteand the staff levels for the last eight consecutive minutes are lessthan the staff presently deployed. If these comparisons indicate that nostaffing changes are required, the system displays a message to hold thepresent staffing.

The processing described is repeated in its entirety each current minuteto generate an updated display each minute showing the lane trafficforecast and staffing requirements.

The lookup table for determining staff requirements may be created fromdata generated from the offline processing shown by the flow diagrams ofFIG. 9. The staffing requirements for a particular store depend on thetraffic lane forecast and certain "optimal" conditions for that store.These conditions include: the average time a buyer will wait in line atthe checkout; the maximum wait a buyer will experience at the checkout;the maximum line length (number of people); percent cashiers' idle time.For example, a particular store may establish the following "optimal"parameters: on average a buyer should wait in line less than 30 seconds;the maximum wait in line should be less than five minutes; the maximumline length should be no more than four people; the percent idle timefor the cashiers should be between 10-15%. These "optimal" conditionswill vary depending on the store, time of day and other conditions.

For any given weighted average forecast of shopper traffic and for agiven number of checkout lanes, the system generates information fromwhich it can be determined whether the optimal conditions aresatisfactorily met. It does so by generating many random shopper arrivalpatterns, and by randomly assigning a shopper checkout time (the timeperiod it takes to check out once the shopper reaches the cashier) foreach buyer from a frequency distribution of shopper checkout times suchas shown in FIG. 7. The frequency distribution may be obtained from thestore POS system which records the checkout transactions and includesdata representing the durations of those transactions.

With reference to FIG. 9A-9C, the computer performs 200 simulations, allassuming the same weighted average shopper traffic (times 10) and agiven number of open checkout lanes. A random traffic pattern is createdover a specified time interval, which in this embodiment is selected astwo hours. The system randomizes the arrival times of the shoppers andestablishes an array of arrival times for each second during thetwo-hour period. It then performs a traffic simulation recording thewait time per shopper, the line length, and the cashier idle time. Fromthe 200 simulations it reports: 1) the maximum time wait by any shopper;2) average wait per shopper; 3) maximum line length; 4) average linelength; and 5) cashier idle time percentage. This information isgenerated from numerous combinations of weighted average forecasts andopen lanes from which a lookup table may be created of checkout lanesstaffing versus weighted average lane traffic forecast with complianceor near compliance of "optimal" conditions.

The system may include any number of lane staffing lookup tables, eachderived using a different frequency distribution of shopper checkouttimes, as the frequency distribution may vary depending on the time ofday, which may be indicative of the fatigue of the checkout lanepersonnel, as well as other factors.

There are various changes and modifications which may be made to theinvention as would be apparent to those skilled in the art. However,these changes or modifications are included in the teaching of thedisclosure, and it is intended that the invention be limited only by thescope of the claims appended hereto.

What is claimed is:
 1. A computer implemented method of forecastingcheckout lane traffic in a store including a recognition system havingat least one sensor to recognize shoppers as they enter the store, themethod comprising the steps of:(a) recognizing, via said at least onesensor, shoppers who enter the store during a time period of selectedduration, said time period composed of time intervals of selecteddurations; (b) generating data, via said recognition system,representing the entry time interval of said shoppers; (c) transmittingsaid data representing the entry time interval of each shopper to acomputer; (d) designating, via said computer, a percentage of saidshoppers as buyers based on a selected conversion rate; (e) assigning,via said computer, a shopping time to each buyer; and (f) establishing,via said computer, a checkout lane arrival time for each buyer based onthe buyer's entry time interval and assigned shopping time.
 2. Themethod of claim 1 further comprising the step of forecasting checkoutlane traffic for a forecast period from the checkout lane arrival times.3. The method of claim 2 further comprising the steps of repeating (d)through (f) to provide multiple simulations and forecasting checkoutlane traffic as a function of said simulations.
 4. The method of claim 3further comprising the step of determining checkout lane staffingrequirements from data generated as a function of a weighted average ofthe checkout lane arrival times of said buyers.
 5. The method of claim 2further comprising the step of producing a graphic display of saidcheckout lane traffic forecast.
 6. The method of claim 5 furthercomprising the step of producing a graphic display of lane traffic for aselected historical time period.
 7. The method of claim 1 furthercomprising the step of determining checkout lane staffing requirementsfrom data generated as a function of said checkout lane trafficforecast.
 8. The method of claim 7 further comprising the step ofdetermining checkout lane staffing requirements as a function ofcriteria relating to checkout times, checkout waiting times, andcheckout personnel idle times.
 9. The method of claim 1 wherein the stepof assigning a shopping time includes assigning said shopping timerandomly from a frequency distribution.
 10. A computer implementedmethod of forecasting checkout lane traffic in a store, said methodcomprising the steps of:(a) electronically recognizing, with theassistance of at least one sensor, shopping data associated with thestore, said shopping data including the time that each shopper entersthe store during a selected time period, the time that each shopperexits the store during a selected time period, and the time of eachtransaction during a selected time period; (b) transmitting saidshopping data to a computer; (c) generating historical data, via saidcomputer, representing historical shopping patterns for said store, saidshopping patterns including the number of shoppers who enter the storeover a selected period, the percentage of said shoppers who are buyers,and the shopping times for said buyers; (d) periodically electronicallyupdating said historical data relating to the number of shoppers whoenter the store to a current time interval; and (e) generating data, viasaid computer, representing a forecast of checkout lane traffic as afunction of said data representing said updated historical shoppingpatterns.
 11. The method of claim 10 further comprising the step ofrecording said shopping data for future accessibility by said computer.12. The method of claim 10 wherein said data representing said checkoutlane traffic forecast is generated in response to repeated simulationsbased on said updated historical data.
 13. The method of claim 10further comprising the step of producing a graphic display of saidcheckout lane traffic forecast.
 14. The method of claim 10 furthercomprising the step of determining checkout lane staffing requirementsas a function of said checkout lane traffic forecast.
 15. A computerimplemented method of forecasting shopper arrival times at one or morecheckout lanes in a store including a recognition system having at leastone sensor to recognize shoppers as they enter the store, said methodcomprising the steps of:recognizing, via said at least one sensor,shoppers who enter the store; generating data, via said recognitionsystem, representing the entry time of each shopper; transmitting saidentry time data to a computer; determining, via said computer, a firstdistribution of shoppers as said shoppers enter the store; andgenerating, via said computer, a second distribution of shoppers fromsaid first distribution of shoppers, said second distribution beingrepresentative of a forecast of said shoppers' checkout lane arrivaltimes.
 16. The method of claim 15 wherein the step of generating asecond distribution of shoppers includes the step of combining saidfirst distribution with a statistical database.
 17. A computerimplemented system for forecasting checkout lane traffic in a store,said system comprising:a recognition system including at least onesensor to recognize shoppers who enter the store during a time period ofselected duration, said time period composed of time intervals ofselected durations, said recognition system generating data representingthe entry time interval of each of said shoppers; a communication linkbetween said recognition system and a computer so that said entry timeinterval data may be transmitted to said computer; and said computerautomatically designating a percentage of said shoppers as buyers basedon a selected conversion rate, automatically assigning a shopping timeto each of said buyers, and automatically establishing a checkout lanearrive time interval for each buyer based on said buyer's entry timeinterval and assigned shopping time so that checkout lane traffic may beforecast.
 18. The system of claim 17 wherein said shopping time isgenerated randomly from a frequency distribution.
 19. A computerimplemented system for automatically forecasting checkout lane trafficin a store, said system comprising:(a) at least one sensor to recognizeshopping activity associated with the store, said shopping activityincluding shoppers entering the store during a selected time period,shoppers exiting the store during a selected time period, andtransactions occurring during a selected time period; (b) acommunication link between said at least one sensor and a computer sothat data representing said shopping activity may be transmitted to saidcomputer; (c) said computer generating historical data representinghistorical shopping patterns for said store, said shopping patternsincluding the number of shoppers who enter the store over a selectedtime period, the percent of said shoppers who are buyers, and theshopping times for said buyers; (d) said computer periodically updatingsaid historical data to a current time interval; and (e) said computerautomatically generating data representing a forecast of checkout lanetraffic as a function of said updated historical data.
 20. The system ofclaim 19 wherein said at least one sensor comprises at least one sensorto recognize shoppers entering and exiting the store, and at least onesensor to recognize transactions.
 21. The system of claim 20 whereinsaid data representing said checkout lane traffic forecast is generatedin response to repeated simulations based on said updated historicaldata.
 22. The system of claim 21 wherein said computer designates thepercentage of said shoppers who are buyers based on a selectedconversion rate and produces a graphic display of said checkout lanetraffic forecast.
 23. The system of claim 22 wherein said computerproduces an updated graphic display of said checkout lane trafficforecast with each updated generation of data representing a forecast ofcheckout lane traffic.
 24. The system of claim 23 wherein said graphicdisplay includes a display of lane traffic for a selected historicaltime period.
 25. The system of claim 22 wherein said computer generatesdata representing checkout lane staffing requirements as a function ofsaid updated data representing a forecast of checkout lane traffic. 26.A computer implemented system for forecasting checkout lane traffic in astore, said system comprising:a recognition system to recognize shoppersas they enter the store and to generate data representing the entry timeof each of said shoppers; a communication link between said recognitionsystem and a computer so that said entry time data may be transmitted tosaid computer; a first distribution of shoppers being determined by saidcomputer as said shoppers enter the store; and a second distribution ofshoppers being generated by said computer, said second distributiongenerated from said first distribution and representative of a forecastof said shoppers' arrival times at said checkout lanes.
 27. The systemof claim 26 wherein said recognition system includes at least one sensorat each entryway.